Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/11531/42362
Registro completo de metadatos
Campo DC Valor Lengua/Idioma
dc.contributor.authorOspina Mateus, Holmanes-ES
dc.contributor.authorQuintana Jiménez, Leonardo Augustoes-ES
dc.contributor.authorLópez Valdés, Francisco Josées-ES
dc.contributor.authorMorales Londoño, Nataliees-ES
dc.contributor.authorSalas Navarro, Katherinnees-ES
dc.date.accessioned2019-10-10T03:10:51Z-
dc.date.available2019-10-10T03:10:51Z-
dc.identifier.urihttp://hdl.handle.net/11531/42362-
dc.description.abstractes-ES
dc.description.abstractObjective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84) and high (16). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results:The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.titleUsing data-mining techniques for the prediction of the severity of road crashes in Cartagena, Colombiaes_ES
dc.typeinfo:eu-repo/semantics/workingPaperes_ES
dc.description.versioninfo:eu-repo/semantics/draftes_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordses-ES
dc.keywordsRoad crashes; Prediction; Data mining; Severityen-GB
Aparece en las colecciones: Documentos de Trabajo

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
IIT-19-101A.pdf187,39 kBAdobe PDFVista previa
Visualizar/Abrir


Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.